TLSAN

This is the implementation for our published paper: TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation. The full-text is also available here: https://www.researchgate.net/publication/349912702_TLSAN_Time-aware_Long-_and_Short-term_Attention_Network_for_Next-item_Recommendation. Here are the brief introductions to the dataset and the experiment results.

Environments

• Python=3.5
• Tensorflow=1.8.0
• Numpy=1.14.2
• Pandas=0.24.1

Datasets

Amazon exposes the official datasets (http://jmcauley.ucsd.edu/data/amazon/) which have filtered out users and items with less than 5 reviews and removed a large amount of invalid data. Because of above advantages, these datasets are widely utilized by researchers. We also chose Amazon's dataset for experiments. In our experiments, only users, items, interactions, and category information are utilized. We do the preprocessing in the following two steps:

1. Remove the users whose interactions less than 10 and the items which interactions less than 8 to ensure the effectiveness of each user and item.
2. Select the users with more than 4 sessions, and select up to 90 behavior records for the remaining users. This step guarantees the existence of long- and short-term behavior records and all behavior records occurred within recent three months.

Statistics (after preprocessing)

Datasets	users	items	categories	samples	avg. items/cate	avg. behaviors/item	avg. behaviors/user
Electronics	39991	22048	673	561100	32.8	25.4	14.0
CDs-Vinyl	24179	27602	310	470087	89.0	17.0	19.4
Clothing-Shoes	2010	1723	226	13157	7.6	7.6	6.5
Digital-Music	1659	1583	53	28852	29.9	18.2	17.4
Office-Products	1720	901	170	29387	5.3	32.6	17.0
Movies-TV	35896	28589	15	752676	1905.9	20.9	26.3
Beauty	3783	2658	179	54225	14.8	20.4	14.3
Home-Kitchen	11567	7722	683	143088	11.3	12.3	18.5
Video-Games	5436	4295	58	83748	74.1	19.5	15.4
Toys-and-Games	2677	2474	221	37515	11.2	15.2	14.0

Experiment results

Datasets	ATRank	BPR-MF	CNN	CSAN–	LSPM	PACA	Bi-LSTM	SHAN	TLSAN
Electronics	0.8659	0.7457	0.8450	0.8005	0.7333	0.8322	0.8495	0.7542	0.9230
CDs-Vinyl	0.8999	0.7684	0.8438	0.7943	0.8594	0.8919	0.8969	0.7138	0.9651
Clothing-Shoes	0.6761	0.6283	0.6712	0.5866	0.6443	0.5313	0.7004	0.7284	0.9363
Digital-Music	0.8601	0.7896	0.8131	0.7685	0.8270	0.9638	0.8468	0.7794	0.9753
Office-Products	0.9162	0.5610	0.8930	0.8401	0.7889	0.8994	0.8628	0.9576	0.9773
Movies-TV	0.8662	0.7654	0.7479	0.7958	0.8003	0.8055	0.8743	0.7771	0.8986
Beauty	0.8160	0.6846	0.7639	0.7620	0.7748	0.9016	0.8231	0.8953	0.9368
Home-Kitchen	0.7039	0.6352	0.7075	0.6820	0.6672	0.8165	0.7373	0.8230	0.8950
Video-Games	0.8809	0.6609	0.8598	0.8033	0.8449	0.8763	0.8598	0.9216	0.9459
Toys-Games	0.8139	0.6294	0.7788	0.7157	0.7708	0.8495	0.8012	0.8797	0.9309

How to run the codes

Download raw data and preprocess it with utils:

sh 0_download_raw.sh
python3 1_convert_pd.py
python3 2_remap_id.py

Build dataset for each model:

python3 build_dataset.py

Train and evaluate the model:

python3 train.py